├── DataSource ├── HSI50_Stock_list.csv ├── StockData │ ├── HK.00001.csv │ ├── HK.00002.csv │ ├── HK.00003.csv │ ├── HK.00005.csv │ ├── HK.00006.csv │ ├── HK.00011.csv │ ├── HK.00012.csv │ ├── HK.00016.csv │ ├── HK.00017.csv │ ├── HK.00027.csv │ ├── HK.00066.csv │ ├── HK.00101.csv │ ├── HK.00175.csv │ ├── HK.00241.csv │ ├── HK.00267.csv │ ├── HK.00288.csv │ ├── HK.00291.csv │ ├── HK.00386.csv │ ├── HK.00388.csv │ ├── HK.00669.csv │ ├── HK.00688.csv │ ├── HK.00700.csv │ ├── HK.00762.csv │ ├── HK.00823.csv │ ├── HK.00857.csv │ ├── HK.00868.csv │ ├── HK.00883.csv │ ├── HK.00939.csv │ ├── HK.00941.csv │ ├── HK.00960.csv │ ├── HK.00968.csv │ ├── HK.01038.csv │ ├── HK.01044.csv │ ├── HK.01093.csv │ ├── HK.01109.csv │ ├── HK.01113.csv │ ├── HK.01177.csv │ ├── HK.01211.csv │ ├── HK.01299.csv │ ├── HK.01398.csv │ ├── HK.01810.csv │ ├── HK.01876.csv │ ├── HK.01928.csv │ ├── HK.01997.csv │ ├── HK.02007.csv │ ├── HK.02018.csv │ ├── HK.02020.csv │ ├── HK.02269.csv │ ├── HK.02313.csv │ ├── HK.02318.csv │ ├── HK.02319.csv │ ├── HK.02331.csv │ ├── HK.02382.csv │ ├── HK.02388.csv │ ├── HK.02628.csv │ ├── HK.02688.csv │ ├── HK.03690.csv │ ├── HK.03968.csv │ ├── HK.03988.csv │ ├── HK.06098.csv │ ├── HK.06862.csv │ ├── HK.09618.csv │ ├── HK.09988.csv │ ├── HK.09999.csv │ └── HK.800000.csv ├── full_hsi_stock_list.csv └── research_use_39_stocks.csv ├── Images ├── optimization-snapshot.png ├── portfolio1.png ├── portfolio2.png ├── prediction-snapshot.png ├── prediction1.png └── prediction2.png ├── LICENSE ├── README.md ├── Research-Cookbook ├── step1-stock_historical_data_download.ipynb ├── step2-stock_data_selection.ipynb ├── step3-stock_prediction_models.ipynb ├── step4-stock_prediction_result_analysis.ipynb ├── step5-stock_portfolio_optimization_models.ipynb └── step6-portfolio_optimization_result_analysis.ipynb ├── Research-Program ├── PortfolioOptimization.py └── StockPrediction.py ├── Results ├── PortfolioOptimization │ ├── portfolio_optimization_results_all_period_prediction.csv │ └── portfolio_optimization_results_all_period_prediction.xlsx ├── PortfolioResultFigures │ ├── [All Time Period]-[Equal Weighted Optimization]-[LSTM Network]].png │ ├── [All Time Period]-[Equal Weighted Optimization]-[Linear Regression]].png │ ├── [All Time Period]-[Equal Weighted Optimization]-[Mean Average]].png │ ├── [All Time Period]-[Equal Weighted Optimization]-[Support Vector Machine(linear)]].png │ ├── [All Time Period]-[Hierarchical Risk Parity Optimization]-[LSTM Network]].png │ ├── [All Time Period]-[Hierarchical Risk Parity Optimization]-[Linear Regression]].png │ ├── [All Time Period]-[Hierarchical Risk Parity Optimization]-[Mean Average]].png │ ├── [All Time Period]-[Hierarchical Risk Parity Optimization]-[Support Vector Machine(linear)]].png │ ├── [All Time Period]-[K-Mean based Mean-Variance Optimization]-[LSTM Network]].png │ ├── [All Time Period]-[K-Mean based Mean-Variance Optimization]-[Linear Regression]].png │ ├── [All Time Period]-[K-Mean based Mean-Variance Optimization]-[Mean Average]].png │ ├── [All Time Period]-[K-Mean based Mean-Variance Optimization]-[Support Vector Machine(linear)]].png │ ├── [All Time Period]-[Mean-Variance Optimization]-[LSTM Network]].png │ ├── [All Time Period]-[Mean-Variance Optimization]-[Linear Regression]].png │ ├── [All Time Period]-[Mean-Variance Optimization]-[Mean Average]].png │ ├── [All Time Period]-[Mean-Variance Optimization]-[Support Vector Machine(linear)]].png │ ├── [Covid Time Period]-[Equal Weighted Optimization]-[LSTM Network]].png │ ├── [Covid Time Period]-[Equal Weighted Optimization]-[Linear Regression]].png │ ├── [Covid Time Period]-[Equal Weighted Optimization]-[Mean Average]].png │ ├── [Covid Time Period]-[Equal Weighted Optimization]-[Support Vector Machine(linear)]].png │ ├── [Covid Time Period]-[Hierarchical Risk Parity Optimization]-[LSTM Network]].png │ ├── [Covid Time Period]-[Hierarchical Risk Parity Optimization]-[Linear Regression]].png │ ├── [Covid Time Period]-[Hierarchical Risk Parity Optimization]-[Mean Average]].png │ ├── [Covid Time Period]-[Hierarchical Risk Parity Optimization]-[Support Vector Machine(linear)]].png │ ├── [Covid Time Period]-[K-Mean based Mean-Variance Optimization]-[LSTM Network]].png │ ├── [Covid Time Period]-[K-Mean based Mean-Variance Optimization]-[Linear Regression]].png │ ├── [Covid Time Period]-[K-Mean based Mean-Variance Optimization]-[Mean Average]].png │ ├── [Covid Time Period]-[K-Mean based Mean-Variance Optimization]-[Support Vector Machine(linear)]].png │ ├── [Covid Time Period]-[Mean-Variance Optimization]-[LSTM Network]].png │ ├── [Covid Time Period]-[Mean-Variance Optimization]-[Linear Regression]].png │ ├── [Covid Time Period]-[Mean-Variance Optimization]-[Mean Average]].png │ ├── [Covid Time Period]-[Mean-Variance Optimization]-[Support Vector Machine(linear)]].png │ ├── [Pre Covid Test Time Period]-[Equal Weighted Optimization]-[LSTM Network]].png │ ├── [Pre Covid Test Time Period]-[Equal Weighted Optimization]-[Linear Regression]].png │ ├── [Pre Covid Test Time Period]-[Equal Weighted Optimization]-[Mean Average]].png │ ├── [Pre Covid Test Time Period]-[Equal Weighted Optimization]-[Support Vector Machine(linear)]].png │ ├── [Pre Covid Test Time Period]-[Hierarchical Risk Parity Optimization]-[LSTM Network]].png │ ├── [Pre Covid Test Time Period]-[Hierarchical Risk Parity Optimization]-[Linear Regression]].png │ ├── [Pre Covid Test Time Period]-[Hierarchical Risk Parity Optimization]-[Mean Average]].png │ ├── [Pre Covid Test Time Period]-[Hierarchical Risk Parity Optimization]-[Support Vector Machine(linear)]].png │ ├── [Pre Covid Test Time Period]-[K-Mean based Mean-Variance Optimization]-[LSTM Network]].png │ ├── [Pre Covid Test Time Period]-[K-Mean based Mean-Variance Optimization]-[Linear Regression]].png │ ├── [Pre Covid Test Time Period]-[K-Mean based Mean-Variance Optimization]-[Mean Average]].png │ ├── [Pre Covid Test Time Period]-[K-Mean based Mean-Variance Optimization]-[Support Vector Machine(linear)]].png │ ├── [Pre Covid Test Time Period]-[Mean-Variance Optimization]-[LSTM Network]].png │ ├── [Pre Covid Test Time Period]-[Mean-Variance Optimization]-[Linear Regression]].png │ ├── [Pre Covid Test Time Period]-[Mean-Variance Optimization]-[Mean Average]].png │ ├── [Pre Covid Test Time Period]-[Mean-Variance Optimization]-[Support Vector Machine(linear)]].png │ ├── [Pre Covid Time Period]-[Equal Weighted Optimization]-[LSTM Network]].png │ ├── [Pre Covid Time Period]-[Equal Weighted Optimization]-[Linear Regression]].png │ ├── [Pre Covid Time Period]-[Equal Weighted Optimization]-[Mean Average]].png │ ├── [Pre Covid Time Period]-[Equal Weighted Optimization]-[Support Vector Machine(linear)]].png │ ├── [Pre Covid Time Period]-[Hierarchical Risk Parity Optimization]-[LSTM Network]].png │ ├── [Pre Covid Time Period]-[Hierarchical Risk Parity Optimization]-[Linear Regression]].png │ ├── [Pre Covid Time Period]-[Hierarchical Risk Parity Optimization]-[Mean Average]].png │ ├── [Pre Covid Time Period]-[Hierarchical Risk Parity Optimization]-[Support Vector Machine(linear)]].png │ ├── [Pre Covid Time Period]-[K-Mean based Mean-Variance Optimization]-[LSTM Network]].png │ ├── [Pre Covid Time Period]-[K-Mean based Mean-Variance Optimization]-[Linear Regression]].png │ ├── [Pre Covid Time Period]-[K-Mean based Mean-Variance Optimization]-[Mean Average]].png │ ├── [Pre Covid Time Period]-[K-Mean based Mean-Variance Optimization]-[Support Vector Machine(linear)]].png │ ├── [Pre Covid Time Period]-[Mean-Variance Optimization]-[LSTM Network]].png │ ├── [Pre Covid Time Period]-[Mean-Variance Optimization]-[Linear Regression]].png │ ├── [Pre Covid Time Period]-[Mean-Variance Optimization]-[Mean Average]].png │ ├── [Pre Covid Time Period]-[Mean-Variance Optimization]-[Support Vector Machine(linear)]].png │ ├── [Time Period]-[Mean-Variance Optimization]-[prediction model]].png │ ├── [Time]-[Hierarchical Risk Parity Optimization]-[Prediction model]].png │ ├── [test]-[K-Mean based Mean-Variance Optimization]-[prediction model]].png │ └── [time period]-[Equal Weighted Optimization]-[Prediction model]].png ├── PredictionResultFigures │ ├── 0why-prediction-results-are-not-complete.txt │ ├── [HK.00002]-[Decision Tree]-[2012-01-01]-[2020-01-09]-[Forward-30-days]-['turnover_rate', 'volume', 'turnover', 'change_rate']-[close].png │ ├── [HK.00002]-[Decision Tree]-[2012-01-01]-[2022-01-01]-[Forward-30-days]-['turnover_rate', 'volume', 'turnover', 'change_rate']-[close].png │ ├── [HK.00002]-[Decision Tree]-[2018-01-09]-[2020-01-01]-[Forward-30-days]-['turnover_rate', 'volume', 'turnover', 'change_rate']-[close].png │ ├── [HK.00002]-[Decision Tree]-[2020-01-09]-[2022-01-01]-[Forward-30-days]-['turnover_rate', 'volume', 'turnover', 'change_rate']-[close].png │ ├── [HK.00002]-[Linear Regression]-[2012-01-01]-[2020-01-09]-[Forward-30-days]-['turnover_rate', 'volume', 'turnover', 'change_rate']-[close].png │ ├── [HK.00002]-[Linear Regression]-[2012-01-01]-[2022-01-01]-[Forward-30-days]-['turnover_rate', 'volume', 'turnover', 'change_rate']-[close].png │ ├── [HK.00002]-[Linear Regression]-[2018-01-09]-[2020-01-01]-[Forward-30-days]-['turnover_rate', 'volume', 'turnover', 'change_rate']-[close].png │ ├── [HK.00002]-[Linear Regression]-[2020-01-09]-[2022-01-01]-[Forward-30-days]-['turnover_rate', 'volume', 'turnover', 'change_rate']-[close].png │ ├── [HK.00002]-[Long Short-Term Memory (LSTM)]-[2012-01-01]-[2020-01-09]-[Forward-30-days]-['turnover_rate', 'volume', 'turnover', 'change_rate']-[close].png │ ├── [HK.00002]-[Long Short-Term Memory (LSTM)]-[2012-01-01]-[2022-01-01]-[Forward-30-days]-['turnover_rate', 'volume', 'turnover', 'change_rate']-[close].png │ ├── [HK.00002]-[Long Short-Term Memory (LSTM)]-[2018-01-09]-[2020-01-01]-[Forward-30-days]-['turnover_rate', 'volume', 'turnover', 'change_rate']-[close].png │ ├── [HK.00002]-[Long Short-Term Memory (LSTM)]-[2020-01-09]-[2022-01-01]-[Forward-30-days]-['turnover_rate', 'volume', 'turnover', 'change_rate']-[close].png │ ├── [HK.00002]-[Random Forest]-[2012-01-01]-[2020-01-09]-[Forward-30-days]-['turnover_rate', 'volume', 'turnover', 'change_rate']-[close].png │ ├── [HK.00002]-[Random Forest]-[2012-01-01]-[2022-01-01]-[Forward-30-days]-['turnover_rate', 'volume', 'turnover', 'change_rate']-[close].png │ ├── [HK.00002]-[Random Forest]-[2018-01-09]-[2020-01-01]-[Forward-30-days]-['turnover_rate', 'volume', 'turnover', 'change_rate']-[close].png │ ├── [HK.00002]-[Random Forest]-[2020-01-09]-[2022-01-01]-[Forward-30-days]-['turnover_rate', 'volume', 'turnover', 'change_rate']-[close].png │ ├── [HK.00002]-[Support Vector Machine (linear)]-[2012-01-01]-[2020-01-09]-[Forward-30-days]-['change_rate']-[close].png │ ├── [HK.00002]-[Support Vector Machine (linear)]-[2012-01-01]-[2022-01-01]-[Forward-30-days]-['change_rate']-[close].png │ ├── [HK.00002]-[Support Vector Machine (linear)]-[2018-01-09]-[2020-01-01]-[Forward-30-days]-['change_rate']-[close].png │ ├── [HK.00002]-[Support Vector Machine (linear)]-[2020-01-09]-[2022-01-01]-[Forward-30-days]-['change_rate']-[close].png │ ├── [HK.00002]-[Support Vector Machine (poly)]-[2012-01-01]-[2020-01-09]-[Forward-30-days]-['change_rate']-[close].png │ ├── [HK.00002]-[Support Vector Machine (poly)]-[2012-01-01]-[2022-01-01]-[Forward-30-days]-['change_rate']-[close].png │ ├── [HK.00002]-[Support Vector Machine (poly)]-[2018-01-09]-[2020-01-01]-[Forward-30-days]-['change_rate']-[close].png │ ├── [HK.00002]-[Support Vector Machine (poly)]-[2020-01-09]-[2022-01-01]-[Forward-30-days]-['change_rate']-[close].png │ ├── [HK.00002]-[Support Vector Machine (rbf)]-[2012-01-01]-[2020-01-09]-[Forward-30-days]-['change_rate']-[close].png │ ├── [HK.00002]-[Support Vector Machine (rbf)]-[2012-01-01]-[2022-01-01]-[Forward-30-days]-['change_rate']-[close].png │ ├── [HK.00002]-[Support Vector Machine (rbf)]-[2018-01-09]-[2020-01-01]-[Forward-30-days]-['change_rate']-[close].png │ ├── [HK.00002]-[Support Vector Machine (rbf)]-[2020-01-09]-[2022-01-01]-[Forward-30-days]-['change_rate']-[close].png │ ├── [HK.00003]-[Decision Tree]-[2012-01-01]-[2020-01-09]-[Forward-30-days]-['turnover_rate', 'volume', 'turnover', 'change_rate']-[close].png │ ├── [HK.00003]-[Decision Tree]-[2012-01-01]-[2022-01-01]-[Forward-30-days]-['turnover_rate', 'volume', 'turnover', 'change_rate']-[close].png │ ├── [HK.00003]-[Decision Tree]-[2018-01-09]-[2020-01-01]-[Forward-30-days]-['turnover_rate', 'volume', 'turnover', 'change_rate']-[close].png │ ├── [HK.00003]-[Decision Tree]-[2020-01-09]-[2022-01-01]-[Forward-30-days]-['turnover_rate', 'volume', 'turnover', 'change_rate']-[close].png │ ├── [HK.00003]-[Linear Regression]-[2012-01-01]-[2020-01-09]-[Forward-30-days]-['turnover_rate', 'volume', 'turnover', 'change_rate']-[close].png │ ├── [HK.00003]-[Linear Regression]-[2012-01-01]-[2022-01-01]-[Forward-30-days]-['turnover_rate', 'volume', 'turnover', 'change_rate']-[close].png │ ├── [HK.00003]-[Linear Regression]-[2018-01-09]-[2020-01-01]-[Forward-30-days]-['turnover_rate', 'volume', 'turnover', 'change_rate']-[close].png │ ├── [HK.00003]-[Linear Regression]-[2020-01-09]-[2022-01-01]-[Forward-30-days]-['turnover_rate', 'volume', 'turnover', 'change_rate']-[close].png │ ├── [HK.00003]-[Long Short-Term Memory (LSTM)]-[2012-01-01]-[2020-01-09]-[Forward-30-days]-['turnover_rate', 'volume', 'turnover', 'change_rate']-[close].png │ ├── [HK.00003]-[Long Short-Term Memory (LSTM)]-[2012-01-01]-[2022-01-01]-[Forward-30-days]-['turnover_rate', 'volume', 'turnover', 'change_rate']-[close].png │ ├── [HK.00003]-[Long Short-Term Memory (LSTM)]-[2018-01-09]-[2020-01-01]-[Forward-30-days]-['turnover_rate', 'volume', 'turnover', 'change_rate']-[close].png │ ├── [HK.00003]-[Long Short-Term Memory (LSTM)]-[2020-01-09]-[2022-01-01]-[Forward-30-days]-['turnover_rate', 'volume', 'turnover', 'change_rate']-[close].png │ ├── [HK.00003]-[Random Forest]-[2012-01-01]-[2020-01-09]-[Forward-30-days]-['turnover_rate', 'volume', 'turnover', 'change_rate']-[close].png │ ├── [HK.00003]-[Random Forest]-[2012-01-01]-[2022-01-01]-[Forward-30-days]-['turnover_rate', 'volume', 'turnover', 'change_rate']-[close].png │ ├── [HK.00003]-[Random Forest]-[2018-01-09]-[2020-01-01]-[Forward-30-days]-['turnover_rate', 'volume', 'turnover', 'change_rate']-[close].png │ ├── [HK.00003]-[Random Forest]-[2020-01-09]-[2022-01-01]-[Forward-30-days]-['turnover_rate', 'volume', 'turnover', 'change_rate']-[close].png │ ├── [HK.00003]-[Support Vector Machine (linear)]-[2012-01-01]-[2020-01-09]-[Forward-30-days]-['change_rate']-[close].png │ ├── [HK.00003]-[Support Vector Machine (linear)]-[2012-01-01]-[2022-01-01]-[Forward-30-days]-['change_rate']-[close].png │ ├── [HK.00003]-[Support Vector Machine (linear)]-[2018-01-09]-[2020-01-01]-[Forward-30-days]-['change_rate']-[close].png │ ├── [HK.00003]-[Support Vector Machine (linear)]-[2020-01-09]-[2022-01-01]-[Forward-30-days]-['change_rate']-[close].png │ ├── [HK.00003]-[Support Vector Machine (poly)]-[2012-01-01]-[2020-01-09]-[Forward-30-days]-['change_rate']-[close].png │ ├── [HK.00003]-[Support Vector Machine (poly)]-[2012-01-01]-[2022-01-01]-[Forward-30-days]-['change_rate']-[close].png │ ├── [HK.00003]-[Support Vector Machine (poly)]-[2018-01-09]-[2020-01-01]-[Forward-30-days]-['change_rate']-[close].png │ ├── [HK.00003]-[Support Vector Machine (poly)]-[2020-01-09]-[2022-01-01]-[Forward-30-days]-['change_rate']-[close].png │ ├── [HK.00003]-[Support Vector Machine (rbf)]-[2012-01-01]-[2020-01-09]-[Forward-30-days]-['change_rate']-[close].png │ ├── [HK.00003]-[Support Vector Machine (rbf)]-[2012-01-01]-[2022-01-01]-[Forward-30-days]-['change_rate']-[close].png │ ├── [HK.00003]-[Support Vector Machine (rbf)]-[2018-01-09]-[2020-01-01]-[Forward-30-days]-['change_rate']-[close].png │ ├── [HK.00003]-[Support Vector Machine (rbf)]-[2020-01-09]-[2022-01-01]-[Forward-30-days]-['change_rate']-[close].png │ ├── [HK.00005]-[Decision Tree]-[2012-01-01]-[2020-01-09]-[Forward-30-days]-['turnover_rate', 'volume', 'turnover', 'change_rate']-[close].png │ ├── [HK.00005]-[Decision Tree]-[2012-01-01]-[2022-01-01]-[Forward-30-days]-['turnover_rate', 'volume', 'turnover', 'change_rate']-[close].png │ ├── [HK.00005]-[Decision Tree]-[2018-01-09]-[2020-01-01]-[Forward-30-days]-['turnover_rate', 'volume', 'turnover', 'change_rate']-[close].png │ ├── [HK.00005]-[Decision Tree]-[2020-01-09]-[2022-01-01]-[Forward-30-days]-['turnover_rate', 'volume', 'turnover', 'change_rate']-[close].png │ ├── [HK.00005]-[Linear Regression]-[2012-01-01]-[2020-01-09]-[Forward-30-days]-['turnover_rate', 'volume', 'turnover', 'change_rate']-[close].png │ ├── [HK.00005]-[Linear Regression]-[2012-01-01]-[2022-01-01]-[Forward-30-days]-['turnover_rate', 'volume', 'turnover', 'change_rate']-[close].png │ ├── [HK.00005]-[Linear Regression]-[2018-01-09]-[2020-01-01]-[Forward-30-days]-['turnover_rate', 'volume', 'turnover', 'change_rate']-[close].png │ ├── [HK.00005]-[Linear Regression]-[2020-01-09]-[2022-01-01]-[Forward-30-days]-['turnover_rate', 'volume', 'turnover', 'change_rate']-[close].png │ ├── [HK.00005]-[Long Short-Term Memory (LSTM)]-[2012-01-01]-[2020-01-09]-[Forward-30-days]-['turnover_rate', 'volume', 'turnover', 'change_rate']-[close].png │ ├── [HK.00005]-[Long Short-Term Memory (LSTM)]-[2012-01-01]-[2022-01-01]-[Forward-30-days]-['turnover_rate', 'volume', 'turnover', 'change_rate']-[close].png │ ├── [HK.00005]-[Long Short-Term Memory (LSTM)]-[2018-01-09]-[2020-01-01]-[Forward-30-days]-['turnover_rate', 'volume', 'turnover', 'change_rate']-[close].png │ ├── [HK.00005]-[Long Short-Term Memory (LSTM)]-[2020-01-09]-[2022-01-01]-[Forward-30-days]-['turnover_rate', 'volume', 'turnover', 'change_rate']-[close].png │ ├── [HK.00005]-[Random Forest]-[2012-01-01]-[2020-01-09]-[Forward-30-days]-['turnover_rate', 'volume', 'turnover', 'change_rate']-[close].png │ ├── [HK.00005]-[Random Forest]-[2012-01-01]-[2022-01-01]-[Forward-30-days]-['turnover_rate', 'volume', 'turnover', 'change_rate']-[close].png │ ├── [HK.00005]-[Random Forest]-[2018-01-09]-[2020-01-01]-[Forward-30-days]-['turnover_rate', 'volume', 'turnover', 'change_rate']-[close].png │ ├── [HK.00005]-[Random Forest]-[2020-01-09]-[2022-01-01]-[Forward-30-days]-['turnover_rate', 'volume', 'turnover', 'change_rate']-[close].png │ ├── [HK.00005]-[Support Vector Machine (linear)]-[2012-01-01]-[2020-01-09]-[Forward-30-days]-['change_rate']-[close].png │ ├── [HK.00005]-[Support Vector Machine (linear)]-[2012-01-01]-[2022-01-01]-[Forward-30-days]-['change_rate']-[close].png │ ├── [HK.00005]-[Support Vector Machine (linear)]-[2018-01-09]-[2020-01-01]-[Forward-30-days]-['change_rate']-[close].png │ ├── [HK.00005]-[Support Vector Machine (linear)]-[2020-01-09]-[2022-01-01]-[Forward-30-days]-['change_rate']-[close].png │ ├── [HK.00005]-[Support Vector Machine (poly)]-[2012-01-01]-[2020-01-09]-[Forward-30-days]-['change_rate']-[close].png │ ├── [HK.00005]-[Support Vector Machine (poly)]-[2012-01-01]-[2022-01-01]-[Forward-30-days]-['change_rate']-[close].png │ ├── [HK.00005]-[Support Vector Machine (poly)]-[2018-01-09]-[2020-01-01]-[Forward-30-days]-['change_rate']-[close].png │ ├── [HK.00005]-[Support Vector Machine (poly)]-[2020-01-09]-[2022-01-01]-[Forward-30-days]-['change_rate']-[close].png │ ├── [HK.00005]-[Support Vector Machine (rbf)]-[2012-01-01]-[2020-01-09]-[Forward-30-days]-['change_rate']-[close].png │ ├── [HK.00005]-[Support Vector Machine (rbf)]-[2012-01-01]-[2022-01-01]-[Forward-30-days]-['change_rate']-[close].png │ ├── [HK.00005]-[Support Vector Machine (rbf)]-[2018-01-09]-[2020-01-01]-[Forward-30-days]-['change_rate']-[close].png │ ├── [HK.00005]-[Support Vector Machine (rbf)]-[2020-01-09]-[2022-01-01]-[Forward-30-days]-['change_rate']-[close].png │ ├── [HK.00006]-[Decision Tree]-[2012-01-01]-[2020-01-09]-[Forward-30-days]-['turnover_rate', 'volume', 'turnover', 'change_rate']-[close].png │ ├── [HK.00006]-[Decision Tree]-[2012-01-01]-[2022-01-01]-[Forward-30-days]-['turnover_rate', 'volume', 'turnover', 'change_rate']-[close].png │ ├── [HK.00006]-[Decision Tree]-[2018-01-09]-[2020-01-01]-[Forward-30-days]-['turnover_rate', 'volume', 'turnover', 'change_rate']-[close].png │ ├── [HK.00006]-[Decision Tree]-[2020-01-09]-[2022-01-01]-[Forward-30-days]-['turnover_rate', 'volume', 'turnover', 'change_rate']-[close].png │ ├── [HK.00006]-[Linear Regression]-[2012-01-01]-[2020-01-09]-[Forward-30-days]-['turnover_rate', 'volume', 'turnover', 'change_rate']-[close].png │ ├── [HK.00006]-[Linear Regression]-[2012-01-01]-[2022-01-01]-[Forward-30-days]-['turnover_rate', 'volume', 'turnover', 'change_rate']-[close].png │ ├── [HK.00006]-[Linear Regression]-[2018-01-09]-[2020-01-01]-[Forward-30-days]-['turnover_rate', 'volume', 'turnover', 'change_rate']-[close].png │ ├── [HK.00006]-[Linear Regression]-[2020-01-09]-[2022-01-01]-[Forward-30-days]-['turnover_rate', 'volume', 'turnover', 'change_rate']-[close].png │ ├── [HK.00006]-[Long Short-Term Memory (LSTM)]-[2012-01-01]-[2020-01-09]-[Forward-30-days]-['turnover_rate', 'volume', 'turnover', 'change_rate']-[close].png │ ├── [HK.00006]-[Long Short-Term Memory (LSTM)]-[2012-01-01]-[2022-01-01]-[Forward-30-days]-['turnover_rate', 'volume', 'turnover', 'change_rate']-[close].png │ ├── [HK.00006]-[Long Short-Term Memory (LSTM)]-[2018-01-09]-[2020-01-01]-[Forward-30-days]-['turnover_rate', 'volume', 'turnover', 'change_rate']-[close].png │ ├── [HK.00006]-[Long Short-Term Memory (LSTM)]-[2020-01-09]-[2022-01-01]-[Forward-30-days]-['turnover_rate', 'volume', 'turnover', 'change_rate']-[close].png │ ├── [HK.00006]-[Random Forest]-[2012-01-01]-[2020-01-09]-[Forward-30-days]-['turnover_rate', 'volume', 'turnover', 'change_rate']-[close].png │ ├── [HK.00006]-[Random Forest]-[2012-01-01]-[2022-01-01]-[Forward-30-days]-['turnover_rate', 'volume', 'turnover', 'change_rate']-[close].png │ ├── [HK.00006]-[Random Forest]-[2018-01-09]-[2020-01-01]-[Forward-30-days]-['turnover_rate', 'volume', 'turnover', 'change_rate']-[close].png │ ├── [HK.00006]-[Random Forest]-[2020-01-09]-[2022-01-01]-[Forward-30-days]-['turnover_rate', 'volume', 'turnover', 'change_rate']-[close].png │ ├── [HK.00006]-[Support Vector Machine (linear)]-[2012-01-01]-[2020-01-09]-[Forward-30-days]-['change_rate']-[close].png │ ├── [HK.00006]-[Support Vector Machine (linear)]-[2012-01-01]-[2022-01-01]-[Forward-30-days]-['change_rate']-[close].png │ ├── [HK.00006]-[Support Vector Machine (linear)]-[2018-01-09]-[2020-01-01]-[Forward-30-days]-['change_rate']-[close].png │ ├── [HK.00006]-[Support Vector Machine (linear)]-[2020-01-09]-[2022-01-01]-[Forward-30-days]-['change_rate']-[close].png │ ├── [HK.00006]-[Support Vector Machine (poly)]-[2012-01-01]-[2020-01-09]-[Forward-30-days]-['change_rate']-[close].png │ ├── [HK.00006]-[Support Vector Machine (poly)]-[2012-01-01]-[2022-01-01]-[Forward-30-days]-['change_rate']-[close].png │ ├── [HK.00006]-[Support Vector Machine (poly)]-[2018-01-09]-[2020-01-01]-[Forward-30-days]-['change_rate']-[close].png │ ├── [HK.00006]-[Support Vector Machine (poly)]-[2020-01-09]-[2022-01-01]-[Forward-30-days]-['change_rate']-[close].png │ ├── [HK.00006]-[Support Vector Machine (rbf)]-[2012-01-01]-[2020-01-09]-[Forward-30-days]-['change_rate']-[close].png │ ├── [HK.00006]-[Support Vector Machine (rbf)]-[2012-01-01]-[2022-01-01]-[Forward-30-days]-['change_rate']-[close].png │ ├── [HK.00006]-[Support Vector Machine (rbf)]-[2018-01-09]-[2020-01-01]-[Forward-30-days]-['change_rate']-[close].png │ └── [HK.00006]-[Support Vector Machine (rbf)]-[2020-01-09]-[2022-01-01]-[Forward-30-days]-['change_rate']-[close].png └── StockPrediction │ ├── Tecnical Indicators Performance Test.csv │ ├── [prediction]-[all_time]-[accuracy].csv │ ├── [prediction]-[all_time_results]-[results].csv │ ├── [prediction]-[all_time_results]-[results].xlsx │ ├── [prediction]-[covid_time]-[accuracy].csv │ ├── [prediction]-[covid_time_results]-[results].csv │ ├── [prediction]-[covid_time_results]-[results].xlsx │ ├── [prediction]-[four period]-[mean_absolute_error].csv │ ├── [prediction]-[four period]-[mean_absolute_error].xlsx │ ├── [prediction]-[four period]-[score].csv │ ├── [prediction]-[four period]-[score].xlsx │ ├── [prediction]-[per_covid_time]-[accuracy].csv │ ├── [prediction]-[pre_covid_test_time]-[accuracy].csv │ ├── [prediction]-[pre_covid_test_time_results]-[results].csv │ ├── [prediction]-[pre_covid_test_time_results]-[results].xlsx │ ├── [prediction]-[pre_covid_time_results]-[results].csv │ ├── [prediction]-[pre_covid_time_results]-[results].xlsx │ ├── all_time_results.csv │ ├── covid_time_results.csv │ ├── portfolio_input_all_period_top5.csv │ ├── portfolio_input_all_period_top5.xlsx │ ├── pre_covid_test_time_results.csv │ └── pre_covid_time_results.csv └── requirements.txt /DataSource/HSI50_Stock_list.csv: -------------------------------------------------------------------------------- 1 | Stock Code,ISIN CODE,Company Name,Industry Classification,Share Type,Weighting (%) 2 | 700,KYG875721634,TENCENT,Information Technology,Other HK-listed Mainland Co.,8 3 | 5,GB0005405286,HSBC HOLDINGS,Financials,HK Ordinary,7.71 4 | 3690,KYG596691041,MEITUAN-W,Information Technology,Other HK-listed Mainland Co.,7.62 5 | 1299,HK0000069689,AIA,Financials,HK Ordinary,7.53 6 | 9988,KYG017191142,BABA-SW,Information Technology,Other HK-listed Mainland Co.,7.13 7 | 939,CNE1000002H1,CCB,Financials,H Share,4.63 8 | 388,HK0388045442,HKEX,Financials,HK Ordinary,4.35 9 | 2318,CNE1000003X6,PING AN,Financials,H Share,2.82 10 | 2269,KYG970081173,WUXI BIO,Healthcare,HK Ordinary,2.64 11 | 1810,KYG9830T1067,XIAOMI-W,Information Technology,Other HK-listed Mainland Co.,2.63 12 | 1398,CNE1000003G1,ICBC,Financials,H Share,2.57 13 | 941,HK0941009539,CHINA MOBILE,Telecommunications,Red Chip,2.28 14 | 3968,CNE1000002M1,CM BANK,Financials,H Share,1.87 15 | 669,HK0669013440,TECHTRONIC IND,Consumer Discretionary,HK Ordinary,1.8 16 | 1211,CNE100000296,BYD COMPANY,Consumer Discretionary,H Share,1.77 17 | 3988,CNE1000001Z5,BANK OF CHINA,Financials,H Share,1.77 18 | 2331,KYG5496K1242,LI NING,Consumer Discretionary,Other HK-listed Mainland Co.,1.52 19 | 2382,KYG8586D1097,SUNNY OPTICAL,Industrials,Other HK-listed Mainland Co.,1.39 20 | 9618,KYG8208B1014,JD-SW,Information Technology,Other HK-listed Mainland Co.,1.34 21 | 2,HK0002007356,CLP HOLDINGS,Utilities,HK Ordinary,1.26 22 | 823,HK0823032773,LINK REIT,Properties & Construction,HK Ordinary,1.14 23 | 883,HK0883013259,CNOOC,Energy,Red Chip,1.14 24 | 3,HK0003000038,HK & CHINA GAS,Utilities,HK Ordinary,1.08 25 | 1,KYG217651051,CKH HOLDINGS,Conglomerates,HK Ordinary,1.07 26 | 2313,KYG8087W1015,SHENZHOU INTL,Consumer Discretionary,Other HK-listed Mainland Co.,1.07 27 | 2020,KYG040111059,ANTA SPORTS,Consumer Discretionary,Other HK-listed Mainland Co.,1 28 | 175,KYG3777B1032,GEELY AUTO,Consumer Discretionary,Other HK-listed Mainland Co.,0.99 29 | 16,HK0016000132,SHK PPT,Properties & Construction,HK Ordinary,0.98 30 | 2319,KYG210961051,MENGNIU DAIRY,Consumer Staples,Red Chip,0.97 31 | 2688,KYG3066L1014,ENN ENERGY,Utilities,Other HK-listed Mainland Co.,0.92 32 | 11,HK0011000095,HANG SENG BANK,Financials,HK Ordinary,0.86 33 | 1109,KYG2108Y1052,CHINA RES LAND,Properties & Construction,Red Chip,0.83 34 | 291,HK0291001490,CHINA RES BEER,Consumer Staples,Red Chip,0.82 35 | 1113,KYG2177B1014,CK ASSET,Properties & Construction,HK Ordinary,0.78 36 | 2628,CNE1000002L3,CHINA LIFE,Financials,H Share,0.76 37 | 2388,HK2388011192,BOC HONG KONG,Financials,HK Ordinary,0.75 38 | 27,HK0027032686,GALAXY ENT,Consumer Discretionary,HK Ordinary,0.7 39 | 386,CNE1000002Q2,SINOPEC CORP,Energy,H Share,0.7 40 | 6098,KYG2453A1085,CG SERVICES,Properties & Construction,Other HK-listed Mainland Co,0.65 41 | 9999,KYG6427A1022,NTES-S,Information Technology,Other HK-listed Mainland Co.,0.64 42 | 66,HK0066009694,MTR CORPORATION,Consumer Discretionary,HK Ordinary,0.62 43 | 1093,HK1093012172,CSPC PHARMA,Healthcare,Other HK-listed Mainland Co.,0.6 44 | 857,CNE1000003W8,PETROCHINA,Energy,H Share,0.58 45 | 1997,KYG9593A1040,WHARF REIC,Properties & Construction,HK Ordinary,0.57 46 | 688,HK0688002218,CHINA OVERSEAS,Properties & Construction,Red Chip,0.56 47 | 6,HK0006000050,POWER ASSETS,Utilities,HK Ordinary,0.53 48 | 960,KYG5635P1090,LONGFOR GROUP,Properties & Construction,Other HK-listed Mainland Co,0.53 49 | 968,KYG9829N1025,XINYI SOLAR,Industrials,Other HK-listed Mainland Co,0.51 50 | 1177,KYG8167W1380,SINO BIOPHARM,Healthcare,Other HK-listed Mainland Co,0.49 51 | 267,HK0267001375,CITIC,Conglomerates,Red Chip,0.44 -------------------------------------------------------------------------------- /DataSource/full_hsi_stock_list.csv: -------------------------------------------------------------------------------- 1 | code,lot_size,stock_name,stock_owner,stock_child_type,stock_type,list_time,stock_id,main_contract,last_trade_time 2 | HK.00001,500,长和,,,STOCK,2015-03-18,4440996184065,False, 3 | HK.00002,500,中电控股,,,STOCK,1970-01-01,2,False, 4 | HK.00003,1000,香港中华煤气,,,STOCK,1970-01-01,3,False, 5 | HK.00005,400,汇丰控股,,,STOCK,1970-01-01,5,False, 6 | HK.00006,500,电能实业,,,STOCK,1976-08-16,6,False, 7 | HK.00011,100,恒生银行,,,STOCK,1972-06-20,3865470566411,False, 8 | HK.00012,1000,恒基地产,,,STOCK,1981-07-23,18124761989132,False, 9 | HK.00016,500,新鸿基地产,,,STOCK,1972-09-08,4209067950096,False, 10 | HK.00017,1000,新世界发展,,,STOCK,1972-11-23,4535485464593,False, 11 | HK.00027,1000,银河娱乐,,,STOCK,1991-10-07,34136400068635,False, 12 | HK.00066,500,港铁公司,,,STOCK,2000-10-05,48249662603330,False, 13 | HK.00101,1000,恒隆地产,,,STOCK,1970-01-01,101,False, 14 | HK.00175,1000,吉利汽车,,,STOCK,1973-02-23,4930622455983,False, 15 | HK.00241,2000,阿里健康,,,STOCK,1972-07-06,3934190043377,False, 16 | HK.00267,1000,中信股份,,,STOCK,1986-02-26,25336012079371,False, 17 | HK.00288,500,万洲国际,,,STOCK,2014-08-05,69947837382944,False, 18 | HK.00291,2000,华润啤酒,,,STOCK,1970-01-01,291,False, 19 | HK.00386,2000,中国石油化工股份,,,STOCK,2000-10-19,48309792145794,False, 20 | HK.00388,100,香港交易所,,,STOCK,2000-06-27,47820165874052,False, 21 | HK.00669,500,创科实业,,,STOCK,1990-12-17,32873679684253,False, 22 | HK.00688,500,中国海外发展,,,STOCK,1992-08-20,35502199669424,False, 23 | HK.00700,100,腾讯控股,,,STOCK,2004-06-16,54047868453564,False, 24 | HK.00762,2000,中国联通,,,STOCK,2000-06-22,47798691037946,False, 25 | HK.00823,100,领展房产基金,,,ETF,2005-11-25,56311316218679,False, 26 | HK.00857,2000,中国石油股份,,,STOCK,2000-04-07,47472273523545,False, 27 | HK.00868,1000,信义玻璃,,,STOCK,2005-02-03,55044300866404,False, 28 | HK.00883,1000,中国海洋石油,,,STOCK,2001-02-28,48876727829363,False, 29 | HK.00939,1000,建设银行,,,STOCK,2005-10-27,56186762167211,False, 30 | HK.00941,500,中国移动,,,STOCK,1997-10-23,43619687859117,False, 31 | HK.00960,500,龙湖集团,,,STOCK,2009-11-19,62560493634496,False, 32 | HK.00968,2000,信义光能,,,STOCK,2013-12-12,68934225101768,False, 33 | HK.01038,500,长江基建集团,,,STOCK,1996-07-17,41631118001166,False, 34 | HK.01044,500,恒安国际,,,STOCK,1998-12-08,45384919417876,False, 35 | HK.01093,2000,石药集团,,,STOCK,1994-06-21,38379827758149,False, 36 | HK.01109,2000,华润置地,,,STOCK,1996-11-08,42120744272981,False, 37 | HK.01113,500,长实集团,,,STOCK,2015-06-03,71244917507161,False, 38 | HK.01177,1000,中国生物制药,,,STOCK,2003-12-08,53227529700505,False, 39 | HK.01211,500,比亚迪股份,,,STOCK,2002-07-31,51101520889019,False, 40 | HK.01299,200,友邦保险,,,STOCK,2010-10-29,64037962384659,False, 41 | HK.01398,1000,工商银行,,,STOCK,2006-10-27,57754425230710,False, 42 | HK.01810,200,小米集团-W,,,STOCK,2018-07-09,76033806042898,False, 43 | HK.01876,100,百威亚太,,,STOCK,2019-09-30,77644418778964,False, 44 | HK.01928,400,金沙中国有限公司,,,STOCK,2009-11-30,62607738275720,False, 45 | HK.01997,1000,九龙仓置业,,,STOCK,2017-11-23,75067438401485,False, 46 | HK.02007,1000,碧桂园,,,STOCK,2007-04-20,58506044508119,False, 47 | HK.02018,500,瑞声科技,,,STOCK,2005-08-09,55847459751906,False, 48 | HK.02020,200,安踏体育,,,STOCK,2007-07-10,58853936859108,False, 49 | HK.02269,500,药明生物,,,STOCK,2017-06-13,74371653699805,False, 50 | HK.02313,100,申洲国际,,,STOCK,2005-11-24,56307021252873,False, 51 | HK.02318,500,中国平安,,,STOCK,2004-06-24,54082228193550,False, 52 | HK.02319,1000,蒙牛乳业,,,STOCK,2004-06-10,54022098651407,False, 53 | HK.02331,500,李宁,,,STOCK,2004-06-28,54099408062747,False, 54 | HK.02382,100,舜宇光学科技,,,STOCK,2007-06-15,58746562677070,False, 55 | HK.02388,500,中银香港,,,STOCK,2002-07-25,51075751086420,False, 56 | HK.02628,1000,中国人寿,,,STOCK,2003-12-18,53270479374916,False, 57 | HK.02688,100,新奥能源,,,STOCK,2002-06-03,50852412787328,False, 58 | HK.03690,100,美团-W,,,STOCK,2018-09-20,76364518526570,False, 59 | HK.03968,500,招商银行,,,STOCK,2006-09-22,57604101377920,False, 60 | HK.03988,1000,中国银行,,,STOCK,2006-06-01,57118770073492,False, 61 | HK.06098,1000,碧桂园服务,,,STOCK,2018-06-19,75965086570450,False, 62 | HK.06862,1000,海底捞,,,STOCK,2018-09-26,76385993366222,False, 63 | HK.09618,50,京东集团-SW,,,STOCK,2020-06-18,79100412700050,False, 64 | HK.09988,100,阿里巴巴-SW,,,STOCK,2019-11-26,78224239372036,False, 65 | HK.09999,100,网易-S,,,STOCK,2020-06-11,79083232831247,False, 66 | -------------------------------------------------------------------------------- /DataSource/research_use_39_stocks.csv: -------------------------------------------------------------------------------- 1 | Stock Code,ISIN CODE,Company Name,Industry Classification,Share Type,Weighting (%),lot_size,stock_name,list_time 2 | HK.00700,KYG875721634,TENCENT,Information Technology,Other HK-listed Mainland Co.,8.0,100,腾讯控股,2004-06-16 3 | HK.00005,GB0005405286,HSBC HOLDINGS,Financials,HK Ordinary,7.71,400,汇丰控股,1970-01-01 4 | HK.01299,HK0000069689,AIA,Financials,HK Ordinary,7.53,200,友邦保险,2010-10-29 5 | HK.00939,CNE1000002H1,CCB,Financials,H Share,4.63,1000,建设银行,2005-10-27 6 | HK.00388,HK0388045442,HKEX,Financials,HK Ordinary,4.35,100,香港交易所,2000-06-27 7 | HK.02318,CNE1000003X6,PING AN,Financials,H Share,2.82,500,中国平安,2004-06-24 8 | HK.01398,CNE1000003G1,ICBC,Financials,H Share,2.57,1000,工商银行,2006-10-27 9 | HK.00941,HK0941009539,CHINA MOBILE,Telecommunications,Red Chip,2.28,500,中国移动,1997-10-23 10 | HK.03968,CNE1000002M1,CM BANK,Financials,H Share,1.87,500,招商银行,2006-09-22 11 | HK.00669,HK0669013440,TECHTRONIC IND,Consumer Discretionary,HK Ordinary,1.8,500,创科实业,1990-12-17 12 | HK.01211,CNE100000296,BYD COMPANY,Consumer Discretionary,H Share,1.77,500,比亚迪股份,2002-07-31 13 | HK.03988,CNE1000001Z5,BANK OF CHINA,Financials,H Share,1.77,1000,中国银行,2006-06-01 14 | HK.02331,KYG5496K1242,LI NING,Consumer Discretionary,Other HK-listed Mainland Co.,1.52,500,李宁,2004-06-28 15 | HK.02382,KYG8586D1097,SUNNY OPTICAL,Industrials,Other HK-listed Mainland Co.,1.39,100,舜宇光学科技,2007-06-15 16 | HK.00002,HK0002007356,CLP HOLDINGS,Utilities,HK Ordinary,1.26,500,中电控股,1970-01-01 17 | HK.00823,HK0823032773,LINK REIT,Properties & Construction,HK Ordinary,1.14,100,领展房产基金,2005-11-25 18 | HK.00883,HK0883013259,CNOOC,Energy,Red Chip,1.14,1000,中国海洋石油,2001-02-28 19 | HK.00003,HK0003000038,HK & CHINA GAS,Utilities,HK Ordinary,1.08,1000,香港中华煤气,1970-01-01 20 | HK.02313,KYG8087W1015,SHENZHOU INTL,Consumer Discretionary,Other HK-listed Mainland Co.,1.07,100,申洲国际,2005-11-24 21 | HK.02020,KYG040111059,ANTA SPORTS,Consumer Discretionary,Other HK-listed Mainland Co.,1.0,200,安踏体育,2007-07-10 22 | HK.00175,KYG3777B1032,GEELY AUTO,Consumer Discretionary,Other HK-listed Mainland Co.,0.99,1000,吉利汽车,1973-02-23 23 | HK.00016,HK0016000132,SHK PPT,Properties & Construction,HK Ordinary,0.98,500,新鸿基地产,1972-09-08 24 | HK.02319,KYG210961051,MENGNIU DAIRY,Consumer Staples,Red Chip,0.97,1000,蒙牛乳业,2004-06-10 25 | HK.02688,KYG3066L1014,ENN ENERGY,Utilities,Other HK-listed Mainland Co.,0.92,100,新奥能源,2002-06-03 26 | HK.00011,HK0011000095,HANG SENG BANK,Financials,HK Ordinary,0.86,100,恒生银行,1972-06-20 27 | HK.01109,KYG2108Y1052,CHINA RES LAND,Properties & Construction,Red Chip,0.83,2000,华润置地,1996-11-08 28 | HK.00291,HK0291001490,CHINA RES BEER,Consumer Staples,Red Chip,0.82,2000,华润啤酒,1970-01-01 29 | HK.02628,CNE1000002L3,CHINA LIFE,Financials,H Share,0.76,1000,中国人寿,2003-12-18 30 | HK.02388,HK2388011192,BOC HONG KONG,Financials,HK Ordinary,0.75,500,中银香港,2002-07-25 31 | HK.00027,HK0027032686,GALAXY ENT,Consumer Discretionary,HK Ordinary,0.7,1000,银河娱乐,1991-10-07 32 | HK.00386,CNE1000002Q2,SINOPEC CORP,Energy,H Share,0.7,2000,中国石油化工股份,2000-10-19 33 | HK.00066,HK0066009694,MTR CORPORATION,Consumer Discretionary,HK Ordinary,0.62,500,港铁公司,2000-10-05 34 | HK.01093,HK1093012172,CSPC PHARMA,Healthcare,Other HK-listed Mainland Co.,0.6,2000,石药集团,1994-06-21 35 | HK.00857,CNE1000003W8,PETROCHINA,Energy,H Share,0.58,2000,中国石油股份,2000-04-07 36 | HK.00688,HK0688002218,CHINA OVERSEAS,Properties & Construction,Red Chip,0.56,500,中国海外发展,1992-08-20 37 | HK.00006,HK0006000050,POWER ASSETS,Utilities,HK Ordinary,0.53,500,电能实业,1976-08-16 38 | HK.00960,KYG5635P1090,LONGFOR GROUP,Properties & Construction,Other HK-listed Mainland Co,0.53,500,龙湖集团,2009-11-19 39 | HK.01177,KYG8167W1380,SINO BIOPHARM,Healthcare,Other HK-listed Mainland Co,0.49,1000,中国生物制药,2003-12-08 40 | HK.00267,HK0267001375,CITIC,Conglomerates,Red Chip,0.44,1000,中信股份,1986-02-26 41 | -------------------------------------------------------------------------------- /Images/optimization-snapshot.png: -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2022 AqrMaxM 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Research-on-Stock-Prediction-based-Portfolio-Optimization 2 | 3 | > An Empirical Study of Optimal Combination of Algorithms for Prediction-Based Portfolio Optimization Model using Machine Learning over Covid-19 Period using HK stock market 4 | 5 | 6 | 7 | ## What is this Project About? 8 | 9 | - This is the final year project conducted myself during the study in Hong Kong Baptist University in 2022 10 | - This project is the repository of a research which focuses on combining `Stock Prediction Algorithms` and `Portfolio Optimization Algorithms` together to maximize the portfolio performance. 11 | - This search uses the Hong Kong Stock Market and also studies the impact of Covid-19 on the stocks and portfolios performance. 12 | 13 | 14 | 15 | ## What does this Repository Include? 16 | 17 | - This Repository includes all the Data Source, Data Cleaning, Stock Prediction Algorithms, Portfolio Optimization Algorithms, Research Steps, Research Results that are needed in this research. 18 | 19 | - [**DataSource**](./DataSource): This research collects historical stock data from Hong Kong Stock Market using [FUTU Open API](https://openapi.futunn.com/futu-api-doc/en/), including all 64 Hang Seng Index Stocks 20 | - [**Research-Cookbook**](./Research-Cookbook): This is the actual practical research implementation step-by-step, from downloading source data, to data cleaning, build up all stock prediction algorithms, portfolio optimization algorithms, and finally conduct data analysis for studying results. 21 | - [**Research-Program**](./Research-Program): As building up all stock prediction models and portfolio optimization models takes much computing power and memory, it may be hard to run these two process inside the jupyter notebook. The two .py programs inside Research-Program can be used to run through all model-building process and output results. 22 | - [**Results**](./Results): All the results from overall research process are stored in this Results folder, where you can find all .csv .xlsx and figures result files. 23 | 24 | 25 | 26 | ## How to Set Up the Environment? 27 | 28 | - You can first download this repository 29 | - as a `.zip` file 30 | - or run `git clone https://github.com/MaxMA2000/Research-on-Stock-Prediction-based-Portfolio-Optimization` in a folder where you want to set this repository. 31 | 32 | - It is recommended to use `conda` or `virtual environment` to run the repository, for detailed installation on different operating system, please refer to: 33 | - [virtualenv Installation](https://virtualenv.pypa.io/en/latest/installation.html) 34 | - [conda Installation](https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html) 35 | - If you use `virtualenv`, you can then activate the environment and `cd` to the local project repository, and then run `pip install -r requirements.txt` to install all dependcies 36 | - If you use `conda`, please note that several packages used in this research can only be installed via `pip` currently, so it is recommended to directly run the notebook and install the dependcies via searching whether it is available on `conda` when the `No module named xxx package` error message shows up. 37 | 38 | 39 | 40 | ## How to Run Through the Research Process? 41 | 42 | - After setting up the environment, you should be able to run through the research process step-by-step by following the notebooks inside [**Research-Cookbook**](./Research-Cookbook) 43 | - **If you want to use re-run the research, please go directly to the step3 inside [Research-Cookbook](./Research-Cookbook), as step1&2 downloads and cleans up the data, the clean data source is already inside the [DataSource](./DataSource) folder** 44 | - **If you want to add more stocks to your research, please go through the step1 to follow the instructions given inside to download extra stock data using[ FUTU OpenD API](https://openapi.futunn.com/futu-api-doc/en/quick/demo.html) or other Financial API, and step2 to perform data cleaning** 45 | - As step3 and step5 requires large computing power and memory, the kernel may stop when you are using a Jupyter notebook, therefore, you can directly run the `.py` program inside [**Research-Program**](./Research-Program) to output the same results. 46 | 47 | 48 | 49 | ## Which Stock Prediction Algorithms and Portfolio Optimization Algorithms are Included in this Research? 50 | 51 | | Stock Prediction Algorithms | Packages Used | Remarks | 52 | | --------------------------------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | 53 | | Linear Regression Model | [scikit-learn Linear Regression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html) | LinearRegression fits a linear model with coefficients w = (w1, …, wp) to minimize the residual sum of squares between the observed targets in the dataset, and the targets predicted by the linear approximation. | 54 | | Decession Tree Regressor | [scikit-learn Decision Tree Regressor](https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeRegressor.html) | A decision tree regressor. The goal is to create a model that predicts the value of a target variable by learning simple decision rules inferred from the data features. A tree can be seen as a piecewise constant approximation. | 55 | | Random Forest Regressor | [scikit-learn Random Forest Regressor](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html) | A random forest is a meta estimator that fits a number of classifying decision trees on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting. | 56 | | Support Vector Machine (kernel = 'linear', 'poly', 'ref') | [scikit-learn Support Vector Machine](https://scikit-learn.org/stable/modules/svm.html) | Support vector machines (SVMs) are a set of supervised learning methods used for classification, regression and outliers detection. This research uses three different kernel functions for building SVM: 'linear', 'poly', 'ref' | 57 | | Long Short-Term Memory Network | [TensorFlow LSTM](https://www.tensorflow.org/api_docs/python/tf/keras/layers/LSTM) | Long short-term memory (LSTM) is an artificial recurrent neural network (RNN) architecture used in the field of deep learning. Previous researches have proven its outstanding performance in stock price prediction | 58 | 59 | | Portfolio Optimization Algorithms | Packages Used | Remarks | 60 | | -------------------------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | 61 | | Equal-Weighted Portfolio | Hard-Coded | Equally divide the weight of each stock in a given portfolio, used as benchmark for comparision | 62 | | Mean-Variance Optimization | [PyPortfolioOpt Mean-Variance](https://pyportfolioopt.readthedocs.io/en/latest/MeanVariance.html) | Classic portfolio optimization approach, first developed in the Modern Portfolio Theory by Harry Max Markowitz | 63 | | Hierarchical Risk Parity Optimization | [PyPortfolioOpt Hierarchical Risk Parity](https://pyportfolioopt.readthedocs.io/en/latest/OtherOptimizers.html#hierarchical-risk-parity-hrp) | Hierarchical Risk Parity is a novel portfolio optimization method developed by Marcos Lopez de Prado, focuses on divide risk | 64 | | K-Mean Clustering based Mean-Variance Optimization | [scikit-learn K-Means](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html)
[PyPortfolioOpt Mean-Variance](https://pyportfolioopt.readthedocs.io/en/latest/MeanVariance.html) | K-Means clustering is an unsupervised machine approach, this algorithm first divides the stocks into different clusters based on risk and return, then select the similar stocks to conduct Mean-Variance Optimization | 65 | 66 | 67 | 68 | ## What are the Research Results Look Like? 69 | 70 | - The **Key Metrics** for defining the success of prediction and optimization algorithms are below: 71 | 72 | | Types of Algorithms | Key Metrics | Purpose | 73 | | ---------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | 74 | | Stock Prediction | [R2 Score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.r2_score.html)
[Mean Absolute Error](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_error.html)
Last day price of certain stock
Predicted future price of certain stock | Find out the models with highest prediction accuracy and select potential growing stocks to form portfolios | 75 | | Portfolio Optimization | [Sharpe Ratio](https://www.investopedia.com/terms/s/sharperatio.asp)
Cumulative Return (and price increase percentage) | Optimize the weight of each stock in a portfolio and find out their performance in balancing risk-and return, and earning ability | 76 | 77 | 78 | 79 | - All the research results are stored in two formats:`figures` and `tables`, and you can find them in the [Results folder](./Results), some of the examples are also shown below: 80 | - `Figures`: [Stock Prediction Figures](./Results/PredictionResultFigures), [Portfolio Optimization Figures](./Results/PortfolioResultFigures) 81 | - `Tables (csv + xlsx)`: [Stock Prediction Tables](./Results/StockPrediction), [Portfolio Optimization Tables](./Results/PortfolioOptimization) 82 | 83 | 84 | 85 | - Stock Prediction 86 | 87 | ![](./Images/prediction1.png) 88 | 89 | ![](./Images/prediction2.png) 90 | 91 | ![](./Images/prediction-snapshot.png) 92 | 93 | 94 | 95 | - Portfolio Optimization 96 | 97 | ![](./Images/portfolio1.png) 98 | 99 | ![](./Images/portfolio2.png) 100 | 101 | ![](./Images/optimization-snapshot.png) 102 | 103 | 104 | 105 | ## Acknowledgement 106 | This research as my final year project has received help and I would like to sincerely thank for their effort: 107 | - [**Ms. Queenie Lee**](https://fds.hkbu.edu.hk/eng/finance/staff/admin-details.jsp?id=queenieHKB&cv=00069&cid=219&cvurl=) for being my final year project supervisor and providing continuous help 108 | - [**Maggie Wong**](https://library.hkbu.edu.hk/about-us/contact-information/staff-directory/maggie-wong/) for providing HKBU Library trainings and research workshops 109 | - [**Robert Martin**](https://github.com/robertmartin8) for building [PyPortfolioOpt package](https://github.com/robertmartin8/PyPortfolioOpt) and helping me with installation problems via GitHub 110 | 111 | -------------------------------------------------------------------------------- /Results/PortfolioOptimization/portfolio_optimization_results_all_period_prediction.xlsx: 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Here are some examples of the results, 3 | And you can output all new results by going through the Cookbook or Programs -------------------------------------------------------------------------------- /Results/PredictionResultFigures/[HK.00002]-[Decision Tree]-[2012-01-01]-[2020-01-09]-[Forward-30-days]-['turnover_rate', 'volume', 'turnover', 'change_rate']-[close].png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MaxMA2000/Research-on-Stock-Prediction-based-Portfolio-Optimization/765a081b0dfc9756bbd4cec268bf287f53cb764a/Results/PredictionResultFigures/[HK.00002]-[Decision Tree]-[2012-01-01]-[2020-01-09]-[Forward-30-days]-['turnover_rate', 'volume', 'turnover', 'change_rate']-[close].png -------------------------------------------------------------------------------- /Results/PredictionResultFigures/[HK.00002]-[Decision Tree]-[2012-01-01]-[2022-01-01]-[Forward-30-days]-['turnover_rate', 'volume', 'turnover', 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'volume']",0.5215038988136715,46.24231637734288 10 | "['pe_ratio', 'turnover']",0.5215372702041496,46.239371475617595 11 | "['pe_ratio', 'change_rate']",0.5218948945404371,46.208025311559574 12 | "['turnover_rate', 'volume']",0.5288798099127756,45.403146617707364 13 | "['turnover_rate', 'turnover']",0.5207003586226373,46.301396142150466 14 | "['turnover_rate', 'change_rate']",0.521260880374574,46.23645479676108 15 | "['volume', 'turnover']",0.5207499169251321,46.29721183275437 16 | "['volume', 'change_rate']",0.5212987526016766,46.23312500651917 17 | "['turnover', 'change_rate']",0.5212703281179638,46.23746182406718 18 | "['pe_ratio', 'turnover_rate', 'volume']",0.5291557680312674,45.34647413400864 19 | "['pe_ratio', 'turnover_rate', 'turnover']",0.521381222841238,46.25229322116658 20 | "['pe_ratio', 'turnover_rate', 'change_rate']",0.5219172696056043,46.20593750193305 21 | "['pe_ratio', 'volume', 'turnover']",0.521421766239086,46.2490538328176 22 | "['pe_ratio', 'volume', 'change_rate']",0.5219554876520116,46.20231375158443 23 | "['pe_ratio', 'turnover', 'change_rate']",0.5219759251023206,46.19975852827786 24 | "['turnover_rate', 'volume', 'turnover']",0.5337095409385605,44.94832817196757 25 | "['turnover_rate', 'volume', 'change_rate']",0.5290575379234876,45.388603002161595 26 | "['turnover_rate', 'turnover', 'change_rate']",0.5210958650449354,46.25207980067154 27 | "['volume', 'turnover', 'change_rate']",0.5211498905118128,46.24802661968622 28 | "['pe_ratio', 'turnover_rate', 'volume', 'turnover']",0.5339611012231287,44.967630766832926 29 | "['pe_ratio', 'turnover_rate', 'volume', 'change_rate']",0.5293670445966464,45.329835081151266 30 | "['pe_ratio', 'turnover_rate', 'turnover', 'change_rate']",0.5218341182094608,46.21024205890224 31 | "['pe_ratio', 'volume', 'turnover', 'change_rate']",0.5218791932315592,46.20747403814001 32 | "['turnover_rate', 'volume', 'turnover', 'change_rate']",0.5340176800519025,44.91163796133372 33 | "['pe_ratio', 'turnover_rate', 'volume', 'turnover', 'change_rate']",0.5342720889271919,44.93169054289354 34 | -------------------------------------------------------------------------------- /Results/StockPrediction/[prediction]-[all_time]-[accuracy].csv: -------------------------------------------------------------------------------- 1 | stock_code,linear_regression: score,decision_tree: score,random_forest: score,support_vector_machine_linear: score,support_vector_machine_poly: score,support_vector_machine_rbf: score,lstm: score,linear_regression: mean_absolute_error,decision_tree: mean_absolute_error,random_forest: mean_absolute_error,support_vector_machine_linear: mean_absolute_error,support_vector_machine_poly: mean_absolute_error,support_vector_machine_rbf: mean_absolute_error,lstm: mean_absolute_error 2 | 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-------------------------------------------------------------------------------- 1 | stock_code,historical_price,linear_regression: future_price,support_vector_machine_linear: future_price,lstm: future_price,mean_future_price 2 | HK.00700,443.37008,477.9600509462192,499.0189005651917,439.076382291667,472.018444601026 3 | HK.00005,46.9,46.83638406013059,46.8614480545007,47.03328175647259,46.91037129036797 4 | HK.01299,78.6,86.03282084875013,87.21940745678283,82.03437985997199,85.09553605516832 5 | HK.00939,5.4,5.347860701005736,5.207756595447643,5.383545516239405,5.313054270897594 6 | HK.00388,455.4,439.8271567662735,465.7108533053225,437.4259932637215,447.6546677784392 7 | HK.02318,56.15,58.89175725729551,58.77921551732511,56.69226200537682,58.121078259999145 8 | HK.01398,4.4,4.371861034134531,4.286229899164844,4.420817935259342,4.359636289519572 9 | HK.00941,46.8,50.83346940730082,50.303088787833175,47.07448529362678,49.40368116292026 10 | 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HK.00267,7.7,7.996064995955423,7.764065521474734,7.852705004930497,7.8709451741202185 41 | -------------------------------------------------------------------------------- /Results/StockPrediction/[prediction]-[all_time_results]-[results].xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MaxMA2000/Research-on-Stock-Prediction-based-Portfolio-Optimization/765a081b0dfc9756bbd4cec268bf287f53cb764a/Results/StockPrediction/[prediction]-[all_time_results]-[results].xlsx -------------------------------------------------------------------------------- /Results/StockPrediction/[prediction]-[covid_time]-[accuracy].csv: -------------------------------------------------------------------------------- 1 | stock_code,linear_regression: score,decision_tree: score,random_forest: score,support_vector_machine_linear: score,support_vector_machine_poly: score,support_vector_machine_rbf: score,lstm: score,linear_regression: 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mean_absolute_error,4.026281282256698,2.3679017671302005 28 | pre covid test time,support_vector_machine_rbf: mean_absolute_error,6.727165764381327,3.737652044210256 29 | pre covid test time,lstm: mean_absolute_error,1.355541056121833,0.9496680548166496 30 | -------------------------------------------------------------------------------- /Results/StockPrediction/[prediction]-[four period]-[mean_absolute_error].xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MaxMA2000/Research-on-Stock-Prediction-based-Portfolio-Optimization/765a081b0dfc9756bbd4cec268bf287f53cb764a/Results/StockPrediction/[prediction]-[four period]-[mean_absolute_error].xlsx -------------------------------------------------------------------------------- /Results/StockPrediction/[prediction]-[four period]-[score].csv: -------------------------------------------------------------------------------- 1 | time period,performance metrics,mean,median 2 | all time,linear_regression: score,0.27212868147002683,0.4198744260665268 3 | all time,decision_tree: score,-1.1960540364349581,-1.1020493820762431 4 | all time,random_forest: score,-0.74551506005021,-0.5791063845307256 5 | all time,support_vector_machine_linear: score,0.37540815087799895,0.4650289650965432 6 | all time,support_vector_machine_poly: score,-3.9810187976137454,-0.7613945342455382 7 | all time,support_vector_machine_rbf: score,-6.038996148160534,-0.7529595236974009 8 | all time,lstm: score,0.9456125302598344,0.9546633233619082 9 | covid time,linear_regression: score,-2.8286937678642827,-1.6528564883550685 10 | covid time,decision_tree: score,-10.000771703114285,-5.058511369304187 11 | covid time,random_forest: score,-6.25097002936061,-3.3761027077295465 12 | covid time,support_vector_machine_linear: score,-3.026539012615276,-1.6878969144796545 13 | covid time,support_vector_machine_poly: score,-4.589190773679034,-2.005634576408216 14 | covid time,support_vector_machine_rbf: score,-9.796238394622716,-4.392865369683266 15 | covid time,lstm: score,0.47042150647723074,0.5989727149134618 16 | pre covid time,linear_regression: score,0.07604003484886257,0.1669266493451826 17 | pre covid time,decision_tree: score,-1.9048561794168342,-0.7239412029254146 18 | pre covid time,random_forest: score,-1.1519153542510396,-0.4909665598971326 19 | pre covid time,support_vector_machine_linear: score,0.14068889413785923,0.216568758743105 20 | pre covid time,support_vector_machine_poly: score,-3.156300263890741,-1.4610406781463752 21 | pre covid time,support_vector_machine_rbf: score,-6.754085919149721,-2.073172124600041 22 | pre covid time,lstm: score,0.9251829520442653,0.9343732350146025 23 | pre covid test time,linear_regression: score,-4.733496621580085,-2.527090481177692 24 | pre covid test time,decision_tree: score,-9.638987090230435,-4.781196291633687 25 | pre covid test time,random_forest: score,-7.906879306487113,-2.8626572949755933 26 | pre covid test time,support_vector_machine_linear: score,-4.103128052748817,-1.8864470235234 27 | pre covid test time,support_vector_machine_poly: score,-4.949788175409125,-1.9845491642216104 28 | pre covid test time,support_vector_machine_rbf: score,-14.155774620044477,-7.306960802605609 29 | pre covid test time,lstm: score,0.4741232718635996,0.6025009954867793 30 | -------------------------------------------------------------------------------- /Results/StockPrediction/[prediction]-[four period]-[score].xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MaxMA2000/Research-on-Stock-Prediction-based-Portfolio-Optimization/765a081b0dfc9756bbd4cec268bf287f53cb764a/Results/StockPrediction/[prediction]-[four period]-[score].xlsx -------------------------------------------------------------------------------- /Results/StockPrediction/[prediction]-[per_covid_time]-[accuracy].csv: -------------------------------------------------------------------------------- 1 | stock_code,linear_regression: score,decision_tree: score,random_forest: score,support_vector_machine_linear: score,support_vector_machine_poly: score,support_vector_machine_rbf: score,lstm: score,linear_regression: mean_absolute_error,decision_tree: mean_absolute_error,random_forest: mean_absolute_error,support_vector_machine_linear: mean_absolute_error,support_vector_machine_poly: mean_absolute_error,support_vector_machine_rbf: mean_absolute_error,lstm: mean_absolute_error 2 | HK.00700,-0.6030242354545909,-2.3563572223378006,-2.100493652596509,-1.0285021921005937,-3.4766566000243824,-2.5581089559989976,0.809887360265424,29.69119831368375,41.91318431168831,40.40726942077922,32.73676009365732,47.56682217633976,42.92935255135592,10.573312836428045 3 | 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https://raw.githubusercontent.com/MaxMA2000/Research-on-Stock-Prediction-based-Portfolio-Optimization/765a081b0dfc9756bbd4cec268bf287f53cb764a/Results/StockPrediction/[prediction]-[pre_covid_test_time_results]-[results].xlsx -------------------------------------------------------------------------------- /Results/StockPrediction/[prediction]-[pre_covid_time_results]-[results].csv: -------------------------------------------------------------------------------- 1 | stock_code,historical_price,linear_regression: future_price,support_vector_machine_linear: future_price,lstm: future_price,mean_future_price 2 | HK.00700,375.81632,335.4109091196087,341.3820426271982,380.3264028776033,352.37311820813676 3 | HK.00005,58.14282,57.82530364400516,57.37314273319446,58.51425323056698,57.90423320258886 4 | HK.01299,82.934,78.11576384734168,79.18894747652261,81.19677471288443,79.5004953455829 5 | HK.00939,5.92942,5.911870982182597,5.676526316614772,5.943599577723742,5.84399895884037 6 | 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HK.00688,27.75,23.818095911128214,23.329660330719705,27.37098053693771,24.839578926261876 37 | HK.00006,52.13,49.8999706176858,48.54477810421994,51.9846475791931,50.14313210036628 38 | HK.00960,34.6331,32.05323126220178,29.819649682398907,32.2142904817462,31.36239047544896 39 | HK.01177,7.173373,8.868594280266354,7.549387597090639,7.293823513803482,7.903935130386825 40 | HK.00267,9.097,9.215769373054902,9.29056933043885,8.989366828918458,9.165235177470736 41 | -------------------------------------------------------------------------------- /Results/StockPrediction/[prediction]-[pre_covid_time_results]-[results].xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MaxMA2000/Research-on-Stock-Prediction-based-Portfolio-Optimization/765a081b0dfc9756bbd4cec268bf287f53cb764a/Results/StockPrediction/[prediction]-[pre_covid_time_results]-[results].xlsx -------------------------------------------------------------------------------- /Results/StockPrediction/covid_time_results.csv: -------------------------------------------------------------------------------- 1 | stock_code,linear_regression: score,linear_regression: mean_absolute_error,linear_regression: last_day_value,linear_regression: future_price,decision_tree: score,decision_tree: mean_absolute_error,decision_tree: last_day_value,decision_tree: future_price,random_forest: score,random_forest: mean_absolute_error,random_forest: last_day_value,random_forest: future_price,support_vector_machine_linear: score,support_vector_machine_linear: mean_absolute_error,support_vector_machine_linear: last_day_value,support_vector_machine_linear: future_price,support_vector_machine_poly: score,support_vector_machine_poly: mean_absolute_error,support_vector_machine_poly: last_day_value,support_vector_machine_poly: future_price,support_vector_machine_rbf: score,support_vector_machine_rbf: mean_absolute_error,support_vector_machine_rbf: last_day_value,support_vector_machine_rbf: future_price,lstm: score,lstm: mean_absolute_error,lstm: last_day_value,lstm: future_price 2 | HK.00700,-6.616543141654666,49.40942235686232,443.37008,507.03725538904087,-14.278175186006937,62.495034999999994,443.37008,525.96814,-17.461850086761252,75.93256999999997,443.37008,505.1002399999999,-3.443764218046309,33.35821093828996,443.37008,484.2189071630019,-2.005634576408216,26.661909598108828,443.37008,484.51970779864774,-13.088475086060452,69.6500750969995,443.37008,527.0405592606719,0.04851751872365351,17.149739682987565,443.37008,438.773098157919 3 | 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HK.00066,-1.9183167039041416,1.5221289071357216,41.85,43.341683327591724,-4.548906148815157,1.9861956521739135,41.85,42.7,-1.7013986187224237,1.4888586956521734,41.85,40.585000000000015,-0.9980935628682879,1.2585292101658483,41.85,42.07851395462319,-0.5530686802820897,1.110781258564721,41.85,42.03004142593177,-1.195724034396222,1.2641253240290538,41.85,42.45274027500974,0.8392588467448224,0.3794867145528608,41.85,41.892341895103456 34 | HK.01093,-0.04181706552118425,0.6534855582466946,8.47,8.598575989703711,-3.077200361948254,1.1058019456521742,8.47,7.3,-1.0171536333368283,0.8402452930434786,8.47,8.05911106,0.1200862842171333,0.591460476335386,8.47,8.497269598166774,0.2414334317066532,0.5459521923864152,8.47,8.476438205387542,-0.3735857182794833,0.6962894682651013,8.47,8.757315581728108,0.9397417440951931,0.16136312426158952,8.47,8.227292179504062 35 | HK.00857,-4.798851984740351,0.5846930877757198,3.47,3.001706846588883,-8.666630699263113,0.7619885869565218,3.47,2.66707,-7.518910989184379,0.7543772391304348,3.47,2.37507,-5.352279756811715,0.6245513487104657,3.47,2.9544067013517674,-4.941262166779705,0.5708747861697157,3.47,2.9972080056440653,-12.214516365195603,0.9035318146825344,3.47,2.6632728236252214,0.7690729609393775,0.11171038474257196,3.47,3.4780114659065005 36 | HK.00688,-0.042825697854765865,0.6004190825958169,18.46,18.292464775847854,-2.060892942452332,1.0615217391304348,18.46,16.78,-0.8708063042779326,0.812663043478261,18.46,17.338,-0.0752497677994437,0.6105881921440401,18.46,18.314571540142612,-0.19930579304134333,0.6341893103103332,18.46,18.025373749413912,-1.4038052066146287,0.9190552458640756,18.46,17.411110660118116,0.39234716723648844,0.4510576098305838,18.46,18.364488067626954 37 | HK.00006,-2.0833947594527173,1.7121858597910384,48.6,45.96893081427193,-12.181603145745507,3.0176086956521746,48.6,48.02,-5.64652289864635,2.313554347826086,48.6,48.7,-2.682484314841638,1.8572802930964887,48.6,45.66014302204366,-3.4848856133154076,2.036937970199596,48.6,45.35568186449807,-7.584416392654502,2.5305996690069033,48.6,48.64881676388313,0.7787283321659235,0.4418234167901841,48.6,49.10317719459534 38 | HK.00960,-3.3346090295646924,3.769410060796653,36.7,41.30936074337343,-4.511837440247019,4.4042,36.7,37.0242,-4.9413603576457445,4.522274130434783,36.7,42.474529999999994,-1.2801060926811774,2.8419330322988547,36.7,39.63939270659231,-0.992101321899379,2.687591621663579,36.7,39.18115210661307,-8.278244601151973,5.248329593867406,36.7,43.096072473211464,0.30684507269495886,1.5425256114657429,36.7,38.28817224303186 39 | HK.01177,-7.845760901596208,1.237169819621837,5.46,6.872513409600261,-14.590833042131253,1.5750699499999998,5.46,7.3467072,-17.333018684179983,1.7633244667391306,5.46,7.66536644,-6.139114330605918,1.1026215319594963,5.46,6.730508912142048,-7.467027392984571,1.2018981927234054,5.46,6.9058812877717095,-22.683921658687833,1.8644629464712394,5.46,9.060072575416267,0.37338504731029587,0.32085964490108326,5.46,5.809576864540577 40 | HK.00267,-0.8032898935763888,0.8937386980514432,7.7,6.8054685999673525,-0.6735973766581906,0.8308804347826088,7.7,8.672,-0.34358930830570533,0.716133695652174,7.7,6.62,-0.6634913308650263,0.8358814308404243,7.7,7.0159324638437734,-0.8311560137609275,0.8991059308534316,7.7,6.942256904134704,-1.178877286564727,0.9041166647991565,7.7,6.7362665313027925,0.8359362203414369,0.23729575988711138,7.7,7.689149995684624 41 | -------------------------------------------------------------------------------- /Results/StockPrediction/portfolio_input_all_period_top5.csv: -------------------------------------------------------------------------------- 1 | time period,model,portfolio stock input 2 | all_time_results,Linear Regression,"['HK.00700', 'HK.02020', 'HK.00669', 'HK.02313', 'HK.02331']" 3 | all_time_results,Support Vector Machine(linear),"['HK.00700', 'HK.02020', 'HK.01211', 'HK.02313', 'HK.00669']" 4 | all_time_results,LSTM Network,"['HK.01299', 'HK.00016', 'HK.01109', 'HK.00011', 'HK.00006']" 5 | all_time_results,Mean Average,"['HK.00700', 'HK.02020', 'HK.02313', 'HK.00669', 'HK.02331']" 6 | covid_time_results,Linear Regression,"['HK.00700', 'HK.01211', 'HK.02020', 'HK.00388', 'HK.02331']" 7 | covid_time_results,Support Vector Machine(linear),"['HK.01211', 'HK.00388', 'HK.00700', 'HK.02020', 'HK.02331']" 8 | covid_time_results,LSTM Network,"['HK.00388', 'HK.02313', 'HK.02318', 'HK.02020', 'HK.02688']" 9 | covid_time_results,Mean Average,"['HK.01211', 'HK.00700', 'HK.00388', 'HK.02020', 'HK.02331']" 10 | pre_covid_time_results,Linear Regression,"['HK.00291', 'HK.00011', 'HK.02313', 'HK.01093', 'HK.01177']" 11 | pre_covid_time_results,Support Vector Machine(linear),"['HK.02020', 'HK.00291', 'HK.01093', 'HK.00011', 'HK.01177']" 12 | pre_covid_time_results,LSTM Network,"['HK.00700', 'HK.02318', 'HK.00002', 'HK.03968', 'HK.00005']" 13 | pre_covid_time_results,Mean Average,"['HK.00291', 'HK.01093', 'HK.00011', 'HK.02318', 'HK.01177']" 14 | pre_covid_test_time_results,Linear Regression,"['HK.00016', 'HK.00011', 'HK.00388', 'HK.02331', 'HK.01211']" 15 | pre_covid_test_time_results,Support Vector Machine(linear),"['HK.00011', 'HK.01211', 'HK.02331', 'HK.02020', 'HK.02388']" 16 | pre_covid_test_time_results,LSTM Network,"['HK.00011', 'HK.00388', 'HK.01211', 'HK.02020', 'HK.00002']" 17 | pre_covid_test_time_results,Mean Average,"['HK.00011', 'HK.01211', 'HK.02331', 'HK.00388', 'HK.00016']" 18 | -------------------------------------------------------------------------------- /Results/StockPrediction/portfolio_input_all_period_top5.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MaxMA2000/Research-on-Stock-Prediction-based-Portfolio-Optimization/765a081b0dfc9756bbd4cec268bf287f53cb764a/Results/StockPrediction/portfolio_input_all_period_top5.xlsx -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | anyio==3.5.0 2 | appnope==0.1.3 3 | argon2-cffi==21.3.0 4 | argon2-cffi-bindings==21.2.0 5 | asttokens==2.0.5 6 | attrs==21.4.0 7 | Babel==2.9.1 8 | backcall==0.2.0 9 | beautifulsoup4==4.11.1 10 | bleach==5.0.0 11 | certifi==2021.10.8 12 | cffi==1.15.0 13 | charset-normalizer==2.0.12 14 | cmake==3.22.4 15 | cycler==0.11.0 16 | debugpy==1.6.0 17 | decorator==5.1.1 18 | defusedxml==0.7.1 19 | entrypoints==0.4 20 | et-xmlfile==1.1.0 21 | executing==0.8.3 22 | fastjsonschema==2.15.3 23 | fonttools==4.32.0 24 | futu-api==6.1.2608 25 | idna==3.3 26 | importlib-resources==5.7.1 27 | ipykernel==6.13.0 28 | ipython==8.2.0 29 | ipython-genutils==0.2.0 30 | jedi==0.18.1 31 | Jinja2==3.1.1 32 | joblib==1.1.0 33 | json5==0.9.6 34 | jsonschema==4.4.0 35 | jupyter-client==7.2.2 36 | jupyter-core==4.10.0 37 | jupyter-server==1.16.0 38 | jupyterlab==3.3.4 39 | jupyterlab-pygments==0.2.2 40 | jupyterlab-server==2.12.0 41 | kiwisolver==1.4.2 42 | MarkupSafe==2.1.1 43 | matplotlib==3.5.1 44 | matplotlib-inline==0.1.3 45 | mistune==0.8.4 46 | nbclassic==0.3.7 47 | nbclient==0.6.0 48 | nbconvert==6.5.0 49 | nbformat==5.3.0 50 | nest-asyncio==1.5.5 51 | notebook==6.4.11 52 | notebook-shim==0.1.0 53 | numpy==1.22.3 54 | openpyxl==3.0.9 55 | packaging==21.3 56 | pandas==1.4.2 57 | pandocfilters==1.5.0 58 | parso==0.8.3 59 | pexpect==4.8.0 60 | pickleshare==0.7.5 61 | Pillow==9.1.0 62 | prometheus-client==0.14.1 63 | prompt-toolkit==3.0.29 64 | protobuf==3.20.0 65 | psutil==5.9.0 66 | ptyprocess==0.7.0 67 | pure-eval==0.2.2 68 | pycparser==2.21 69 | pycryptodome==3.14.1 70 | Pygments==2.11.2 71 | pyparsing==3.0.8 72 | pyrsistent==0.18.1 73 | python-dateutil==2.8.2 74 | pytz==2022.1 75 | pyzmq==22.3.0 76 | requests==2.27.1 77 | scikit-learn==1.0.2 78 | scipy==1.8.0 79 | Send2Trash==1.8.0 80 | simplejson==3.17.6 81 | six==1.16.0 82 | sniffio==1.2.0 83 | soupsieve==2.3.2.post1 84 | stack-data==0.2.0 85 | terminado==0.13.3 86 | threadpoolctl==3.1.0 87 | tinycss2==1.1.1 88 | tornado==6.1 89 | traitlets==5.1.1 90 | urllib3==1.26.9 91 | wcwidth==0.2.5 92 | webencodings==0.5.1 93 | websocket-client==1.3.2 94 | zipp==3.8.0 95 | pyportfolioopt==1.5.1 96 | pyfolio==0.9.2 97 | --------------------------------------------------------------------------------